def test_resolve_metric_bundle_with_nonexistent_metric():
df = pd.DataFrame({"a": [1, 2, 3, None]})
# Building engine and configurations in attempt to resolve metrics
engine = PandasExecutionEngine(batch_data_dict={"made_up_id": df})
mean = MetricConfiguration(
metric_name="column.i_don't_exist",
metric_domain_kwargs={"column": "a"},
metric_value_kwargs=dict(),
)
stdev = MetricConfiguration(
metric_name="column.nonexistent",
metric_domain_kwargs={"column": "a"},
metric_value_kwargs=dict(),
)
desired_metrics = (mean, stdev)
with pytest.raises(MetricProviderError) as e:
metrics = engine.resolve_metrics(metrics_to_resolve=desired_metrics)
def test_resolve_metric_bundle():
df = pd.DataFrame({"a": [1, 2, 3, None]})
# Building engine and configurations in attempt to resolve metrics
engine = PandasExecutionEngine(batch_data_dict={"made-up-id": df})
metrics: dict = {}
table_columns_metric: MetricConfiguration
results: dict
table_columns_metric, results = get_table_columns_metric(engine=engine)
metrics.update(results)
mean = MetricConfiguration(
metric_name="column.mean",
metric_domain_kwargs={"column": "a"},
metric_value_kwargs=dict(),
metric_dependencies={
"table.columns": table_columns_metric,
},
)
stdev = MetricConfiguration(
metric_name="column.standard_deviation",
metric_domain_kwargs={"column": "a"},
metric_value_kwargs=dict(),
metric_dependencies={
"table.columns": table_columns_metric,
},
)
desired_metrics = (mean, stdev)
results = engine.resolve_metrics(metrics_to_resolve=desired_metrics,
metrics=metrics)
metrics.update(results)
# Ensuring metrics have been properly resolved
assert (metrics[("column.mean", "column=a",
())] == 2.0), "mean metric not properly computed"
assert metrics[("column.standard_deviation", "column=a",
())] == 1.0, ("standard deviation "
"metric not properly computed")